A combined method based on improved principal component analysis(PCA) and integer wavelet transform is proposed for hyperspectral image compression. PCA can effectively reduce the spectral correlation of hyperspectral image and integer wavelet transform by using lift scheme is widely used for spatial decorrelation. The code speed dramatically decreases when the spatial size becomes large. The hyperspectral images are partitioned into several blocks with same size and each block is encoded by PCA and integer wavelet transform independently. A non-linear model is setup to estimate the optimal retained number of principal component(PC) at any compression ratio. When the optimized compression methods are using on the hyperspectral images of the AVIRIS instrument and our developing hyperspectral imager, the compression effects is competitive and it runs fast comparing with common PCA followed by integer wavelet transform. This method is also easily completed on the hardware.